The classification of mental disorder is a subject with important implications for research, clinical decision-making and health care economics. One particular area of controversy is whether the observed variability in clinical features is best understood as a result of a variety of categories of illness (diagnoses) or variations in the magnitude of dimensions of illness. This issue has been especially prominent in developing subtypes of depression, and in evaluating potential boundaries between depression and normality, personality disorder and normality, and schizophrenic, affective and schizoaffective disorders. Both categorical and dimensional approaches have been employed to produce classificatory systems, and within specific areas of psychopathology, systems of both types have received some empirical support. Presumably, for a given area of psychopathology, one of these approaches best represents the structure of nature. However, at this point in time, no methods exist to empirically test the relative appropriateness of the two approaches. One reason for this being a difficult problem is that classification is not directly observed, but is inferred from clinical features. Our previous work has developed statistical models which represent the relationships between unobserved classifications and the presence or absence of observed clinical features. Latent trait analysis is the appropriate model for dimensional classification and latent class analysis the appropriate model for categorical classification. The proposed project will extend this work and make available new methods which will allow the comparison of these two models and the hypotheses they represent. First, the usual latent class model will be altered to simulate the dimensional case. Second, the latent trait model will be altered to simulate the categorical case. In each of these strategies, both types of classification will be represented within the parameterization of a single model, thereby making possible their direct statistical comparison. Funding will support the derivation of maximum likelihood equations for these models and the corresponding alteration of current latent trait and latent class computer programs. These procedures will then be tested by applying them to previously collected data on the clinical features of 788 subjects with major depressive disorder.